






'''simple docstring'''
import enum
import warnings

from ..tokenization_utils import TruncationStrategy
from ..utils import add_end_docstrings, is_tf_available, is_torch_available, logging
from .base import PIPELINE_INIT_ARGS, Pipeline


if is_tf_available():
  import tensorflow as tf

  from ..models.auto.modeling_tf_auto import TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING

if is_torch_available():
  from ..models.auto.modeling_auto import MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING

a_											=			logging.get_logger(__name__)






class   __SCREAMING_SNAKE_CASE   (      enum.Enum					):
 snake_case_					=				0
 snake_case_					=				1






@add_end_docstrings(_A					)
class   __SCREAMING_SNAKE_CASE   (      _A					):
 snake_case_					=				"""generated"""




 def __init__(   self						:				Tuple       ,   *__lowercase						:				Optional[Any]       ,   **__lowercase						:				Optional[int]   )						->    Dict:
       super().__init__(*UpperCamelCase__       ,   **UpperCamelCase__   )

       self.check_model_type(
           TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING
           if self.framework == '''tf'''
           else MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING   )




 def   __magic_name__						(   self						:				List[str]       ,   __lowercase						:				List[str]=None       ,   __lowercase						:				Union[str, Any]=None       ,   __lowercase						:				Dict=None       ,   __lowercase						:				Any=None       ,   __lowercase						:				List[Any]=None       ,   __lowercase						:				Tuple=None       ,   **__lowercase						:				List[str]       ,   )						->    Tuple:
       SCREAMING_SNAKE_CASE__  :					Optional[int]						={}
       if truncation is not None:
             SCREAMING_SNAKE_CASE__  :					int						=truncation

       SCREAMING_SNAKE_CASE__  :					List[str]						=generate_kwargs

       SCREAMING_SNAKE_CASE__  :					Tuple						={}
       if return_tensors is not None and return_type is None:
             SCREAMING_SNAKE_CASE__  :					Optional[int]						=ReturnType.TENSORS if return_tensors else ReturnType.TEXT
       if return_type is not None:
             SCREAMING_SNAKE_CASE__  :					Tuple						=return_type

       if clean_up_tokenization_spaces is not None:
             SCREAMING_SNAKE_CASE__  :					List[Any]						=clean_up_tokenization_spaces

       if stop_sequence is not None:
             SCREAMING_SNAKE_CASE__  :					Dict						=self.tokenizer.encode(UpperCamelCase__       ,   add_special_tokens=UpperCamelCase__   )
             if len(UpperCamelCase__   ) > 1:
                   warnings.warn(
                       '''Stopping on a multiple token sequence is not yet supported on transformers. The first token of'''
                       ''' the stop sequence will be used as the stop sequence string in the interim.'''   )
             SCREAMING_SNAKE_CASE__  :					int						=stop_sequence_ids[0]

       return preprocess_params, forward_params, postprocess_params




 def   __magic_name__						(   self						:				Dict       ,   __lowercase						:				int       ,   __lowercase						:				int       ,   __lowercase						:				int   )						->    Tuple:
       return True




 def   __magic_name__						(   self						:				Dict       ,   *__lowercase						:				str       ,   __lowercase						:				int   )						->    List[Any]:
       SCREAMING_SNAKE_CASE__  :					Optional[int]						=self.model.config.prefix if self.model.config.prefix is not None else ''''''
       if isinstance(args[0]       ,   UpperCamelCase__   ):
             if self.tokenizer.pad_token_id is None:
                   raise ValueError('''Please make sure that the tokenizer has a pad_token_id when using a batch input'''   )
             SCREAMING_SNAKE_CASE__  :					Dict						=([prefix + arg for arg in args[0]],)
             SCREAMING_SNAKE_CASE__  :					int						=True

       elif isinstance(args[0]       ,   UpperCamelCase__   ):
             SCREAMING_SNAKE_CASE__  :					Union[str, Any]						=(prefix + args[0],)
             SCREAMING_SNAKE_CASE__  :					Any						=False
       else:
             raise ValueError(
                 F" `args[0]`: {args[0]} have the wrong format. The should be either of type `str` or type `list`"   )
       SCREAMING_SNAKE_CASE__  :					Optional[Any]						=self.tokenizer(*UpperCamelCase__       ,   padding=UpperCamelCase__       ,   truncation=UpperCamelCase__       ,   return_tensors=self.framework   )
       # This is produced by tokenizers but is an invalid generate kwargs
       if "token_type_ids" in inputs:
             del inputs["token_type_ids"]
       return inputs




 def __call__(   self						:				Tuple       ,   *__lowercase						:				Optional[Any]       ,   **__lowercase						:				Optional[Any]   )						->    Dict:
       SCREAMING_SNAKE_CASE__  :					List[str]						=super().__call__(*UpperCamelCase__       ,   **UpperCamelCase__   )
       if (
           isinstance(args[0]       ,   UpperCamelCase__   )
           and all(isinstance(UpperCamelCase__       ,   UpperCamelCase__   ) for el in args[0]   )
           and all(len(UpperCamelCase__   ) == 1 for res in result   )
       ):
             return [res[0] for res in result]
       return result




 def   __magic_name__						(   self						:				str       ,   __lowercase						:				Optional[int]       ,   __lowercase						:				List[str]=TruncationStrategy.DO_NOT_TRUNCATE       ,   **__lowercase						:				Any   )						->    Optional[int]:
       SCREAMING_SNAKE_CASE__  :					str						=self._parse_and_tokenize(UpperCamelCase__       ,   truncation=UpperCamelCase__       ,   **UpperCamelCase__   )
       return inputs




 def   __magic_name__						(   self						:				Any       ,   __lowercase						:				Any       ,   **__lowercase						:				Tuple   )						->    Tuple:






       if self.framework == "pt":
             SCREAMING_SNAKE_CASE__		,						SCREAMING_SNAKE_CASE__  :					Optional[int]						=model_inputs['''input_ids'''].shape
       elif self.framework == "tf":
             SCREAMING_SNAKE_CASE__		,						SCREAMING_SNAKE_CASE__  :					List[Any]						=tf.shape(model_inputs['''input_ids''']   ).numpy()

       SCREAMING_SNAKE_CASE__  :					Optional[Any]						=generate_kwargs.get('''min_length'''       ,   self.model.config.min_length   )
       SCREAMING_SNAKE_CASE__  :					Tuple						=generate_kwargs.get('''max_length'''       ,   self.model.config.max_length   )
       self.check_inputs(UpperCamelCase__       ,   generate_kwargs['''min_length''']       ,   generate_kwargs['''max_length''']   )
       SCREAMING_SNAKE_CASE__  :					Optional[Any]						=self.model.generate(**UpperCamelCase__       ,   **UpperCamelCase__   )
       SCREAMING_SNAKE_CASE__  :					Union[str, Any]						=output_ids.shape[0]
       if self.framework == "pt":
             SCREAMING_SNAKE_CASE__  :					List[str]						=output_ids.reshape(UpperCamelCase__       ,   out_b // in_b       ,   *output_ids.shape[1:]   )
       elif self.framework == "tf":
             SCREAMING_SNAKE_CASE__  :					Union[str, Any]						=tf.reshape(UpperCamelCase__       ,   (in_b, out_b // in_b, *output_ids.shape[1:])   )
       return {"output_ids": output_ids}




 def   __magic_name__						(   self						:				List[Any]       ,   __lowercase						:				List[Any]       ,   __lowercase						:				List[Any]=ReturnType.TEXT       ,   __lowercase						:				Optional[int]=False   )						->    Optional[int]:
       SCREAMING_SNAKE_CASE__  :					Any						=[]
       for output_ids in model_outputs["output_ids"][0]:
             if return_type == ReturnType.TENSORS:
                   SCREAMING_SNAKE_CASE__  :					Optional[Any]						={F"{self.return_name}_token_ids": output_ids}
             elif return_type == ReturnType.TEXT:
                   SCREAMING_SNAKE_CASE__  :					Optional[int]						={
                       F"{self.return_name}_text": self.tokenizer.decode(
                           UpperCamelCase__       ,   skip_special_tokens=UpperCamelCase__       ,   clean_up_tokenization_spaces=UpperCamelCase__       ,   )
                   }
             records.append(UpperCamelCase__   )
       return records






@add_end_docstrings(_A					)
class   __SCREAMING_SNAKE_CASE   (      _A					):
 snake_case_					=				"""summary"""




 def __call__(   self						:				Any       ,   *__lowercase						:				List[Any]       ,   **__lowercase						:				Dict   )						->    str:
       return super().__call__(*UpperCamelCase__       ,   **UpperCamelCase__   )




 def   __magic_name__						(   self						:				Any       ,   __lowercase						:				int       ,   __lowercase						:				int       ,   __lowercase						:				int   )						->    bool:






       if max_length < min_length:
             logger.warning(F"Your min_length={min_length} must be inferior than your max_length={max_length}."   )

       if input_length < max_length:
             logger.warning(
                 F"Your max_length is set to {max_length}, but your input_length is only {input_length}. Since this is "
                 '''a summarization task, where outputs shorter than the input are typically wanted, you might '''
                 F"consider decreasing max_length manually, e.g. summarizer(\'...\', max_length={input_length//2})"   )






@add_end_docstrings(_A					)
class   __SCREAMING_SNAKE_CASE   (      _A					):
 snake_case_					=				"""translation"""




 def   __magic_name__						(   self						:				Optional[Any]       ,   __lowercase						:				int       ,   __lowercase						:				int       ,   __lowercase						:				int   )						->    Any:






       if input_length > 0.9 * max_length:
             logger.warning(
                 F"Your input_length: {input_length} is bigger than 0.9 * max_length: {max_length}. You might consider "
                 '''increasing your max_length manually, e.g. translator(\'...\', max_length=400)'''   )
       return True




 def   __magic_name__						(   self						:				Optional[Any]       ,   *__lowercase						:				Any       ,   __lowercase						:				List[str]=TruncationStrategy.DO_NOT_TRUNCATE       ,   __lowercase						:				List[str]=None       ,   __lowercase						:				int=None   )						->    Union[str, Any]:






       if getattr(self.tokenizer       ,   '''_build_translation_inputs'''       ,   UpperCamelCase__   ):
             return self.tokenizer._build_translation_inputs(
                 *UpperCamelCase__       ,   return_tensors=self.framework       ,   truncation=UpperCamelCase__       ,   src_lang=UpperCamelCase__       ,   tgt_lang=UpperCamelCase__   )
       else:
             return super()._parse_and_tokenize(*UpperCamelCase__       ,   truncation=UpperCamelCase__   )




 def   __magic_name__						(   self						:				Any       ,   __lowercase						:				Tuple=None       ,   __lowercase						:				List[str]=None       ,   **__lowercase						:				str   )						->    str:
       SCREAMING_SNAKE_CASE__		,						SCREAMING_SNAKE_CASE__		,						SCREAMING_SNAKE_CASE__  :					Optional[Any]						=super()._sanitize_parameters(**UpperCamelCase__   )
       if src_lang is not None:
             SCREAMING_SNAKE_CASE__  :					Union[str, Any]						=src_lang
       if tgt_lang is not None:
             SCREAMING_SNAKE_CASE__  :					str						=tgt_lang
       if src_lang is None and tgt_lang is None:
             # Backward compatibility, direct arguments use is preferred.
             SCREAMING_SNAKE_CASE__  :					Any						=kwargs.get('''task'''       ,   self.task   )
             SCREAMING_SNAKE_CASE__  :					List[str]						=task.split('''_'''   )
             if task and len(UpperCamelCase__   ) == 4:
                   # translation, XX, to YY
                   SCREAMING_SNAKE_CASE__  :					Union[str, Any]						=items[1]
                   SCREAMING_SNAKE_CASE__  :					int						=items[3]
       return preprocess_params, forward_params, postprocess_params




 def __call__(   self						:				List[Any]       ,   *__lowercase						:				int       ,   **__lowercase						:				Optional[Any]   )						->    Union[str, Any]:
       return super().__call__(*UpperCamelCase__       ,   **UpperCamelCase__   )

import string

def a__       (			A_   ):






  '''simple docstring'''





  __magic_name__								=			""""""
  for i in sequence:
    __magic_name__								=			ord(A_   )
    if 65 <= extract <= 90:
      output += chr(155 - extract   )
    elif 97 <= extract <= 122:
      output += chr(219 - extract   )
    else:
      output += i
  return output

def a__       (			A_   ):






  '''simple docstring'''





  __magic_name__								=			string.ascii_letters
  __magic_name__								=			string.ascii_lowercase[::-1] + string.ascii_uppercase[::-1]
  return "".join(
      letters_reversed[letters.index(A_   )] if c in letters else c for c in sequence   )

def a__       (			):






  '''simple docstring'''





  from timeit import timeit

  print("""Running performance benchmarks..."""   )
  __magic_name__								=			"""from string import printable ; from __main__ import atbash, atbash_slow"""
  print(f'''> atbash_slow(): {timeit('atbash_slow(printable)',	setup=A_   )} seconds'''   )
  print(f'''>      atbash(): {timeit('atbash(printable)',	setup=A_   )} seconds'''   )


if __name__ == "__main__":
    for example in ("ABCDEFGH", "123GGjj", "testStringtest", "with space"):
        print(F'''{example} encrypted in atbash: {atbash(example)}''')
    benchmark()

